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Steady-State Modeling of Fuel Cells Based on Atom Search Optimizer

Author

Listed:
  • Ahmed M. Agwa

    (Electrical Engineering Department, Faculty of Engineering, Northern Border University, Arar 73222, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo 11651, Egypt)

  • Attia A. El-Fergany

    (Electrical Power and Machines Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt)

  • Gamal M. Sarhan

    (Electrical Engineering Department, Faculty of Engineering, Northern Border University, Arar 73222, Saudi Arabia)

Abstract

In simulation studies, the precision of fuel cell models has a vital role in the quality of results. Unfortunately, due to the shortage of manufacturer data given in the datasheets, several unknown parameters should be defined to establish the fuel cell model for further precise analysis. This research addresses a novel application of the atom search optimization (ASO) algorithm to generate these unknown parameters of the fuel cell model and in particular of the polymer exchange membrane (PEM) type. The objective of this study is to establish an accurate model of the PEM fuel cells, which will provide accurate results of modeling and simulation in a steady-state condition. Simulations and further demonstrations were performed under MATLAB/SIMULINK. The viability of the proposed models was appraised by comparing its simulation results with the experimental results of number of commercial PEM fuel cells. In the same context, the obtained numerical results by the proposed ASO-based method were compared to other challenging optimization methods-based results. Finally, parametric tests were made which indicated the robustness of the ASO results as well. It can be stated here that ASO performs well and has a good capability to extract the unknown parameters with lesser errors.

Suggested Citation

  • Ahmed M. Agwa & Attia A. El-Fergany & Gamal M. Sarhan, 2019. "Steady-State Modeling of Fuel Cells Based on Atom Search Optimizer," Energies, MDPI, vol. 12(10), pages 1-14, May.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1884-:d:232048
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    References listed on IDEAS

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    Cited by:

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    6. Ángel Encalada-Dávila & Samir Echeverría & Jordy Santana-Villamar & Gabriel Cedeño & Mayken Espinoza-Andaluz, 2021. "Optimization Algorithms: Optimal Parameters Computation for Modeling the Polarization Curves of a PEFC Considering the Effect of the Relative Humidity," Energies, MDPI, vol. 14(18), pages 1-21, September.
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    10. Miao, Di & Chen, Wei & Zhao, Wei & Demsas, Tekle, 2020. "Parameter estimation of PEM fuel cells employing the hybrid grey wolf optimization method," Energy, Elsevier, vol. 193(C).
    11. Junyang Ding & Wenxian Liu & Shusheng Zhang & Jun Luo & Xijun Liu, 2023. "A Mini Review: Recent Advances in Asymmetrically Coordinated Atom Sites for High-Efficiency Hydrogen Evolution Reaction," Energies, MDPI, vol. 16(6), pages 1-18, March.
    12. Mohamed Louzazni & Sameer Al-Dahidi & Marco Mussetta, 2020. "Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique," Sustainability, MDPI, vol. 12(19), pages 1-23, October.
    13. Seleem, Sameh I. & Hasanien, Hany M. & El-Fergany, Attia A., 2021. "Equilibrium optimizer for parameter extraction of a fuel cell dynamic model," Renewable Energy, Elsevier, vol. 169(C), pages 117-128.
    14. Abdel-Basset, Mohamed & Mohamed, Reda & El-Fergany, Attia & Chakrabortty, Ripon K. & Ryan, Michael J., 2021. "Adaptive and efficient optimization model for optimal parameters of proton exchange membrane fuel cells: A comprehensive analysis," Energy, Elsevier, vol. 233(C).
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